Accurate and efficient regression modeling for microarchitectural performance and power prediction

We propose regression modeling as an efficient approach for accurately predicting performance and power for various applications executing on any microprocessor configuration in a large micro-architectural design space. This paper addresses fundamental challenges in microarchitectural simulation cost by reducing the number of required simulations and using simulated results more effectively via statistical modeling and inference. Specifically, we derive and validate regression models for performance and power. Such models enable computationally efficient statistical inference, requiring the simulation of only 1 in 5 million points of a joint microarchitecture-application design space while achieving median error rates as low as 4.1 percent for performance and 4.3 percent for power. Although both models achieve similar accuracy, the sources of accuracy are strikingly different. We present optimizations for a baseline regression model to obtain ( 1) application-specific models to maximize accuracy in performance prediction and ( 2) regional power models leveraging only the most relevant samples from the microarchitectural design space to maximize accuracy in power prediction. Assessing sensitivity to the number of samples simulated for model formulation, we find fewer than 4,000 samples from a design space of approximately 22 billion points are sufficient. Collectively, our results suggest significant potential in accurate and efficient statistical inference for microarchitectural design space exploration via regression models.